It depends on what information you want. The CI describes the population you sampled from, the standard deviation can describe both the sample and the population (if we take the sample standard deviation $s$ to be a good estimator $\hat\sigma$ of the true population value $\sigma$).
Looking at your question, you seem to be bumping into one of those times when casual vocabulary and statistical vocabulary trample on each other's toes. What you call "specific study population" is your sample and the "total population" is simply population.
With that terminological note, using confidence interval to describe your sample isn't appropriate because the sample is finite and "complete" and you use descriptive statistics (standard deviation,etc.) on completely sampled groups. Inferential statistics such as the CI should only be used make statements about the "incomplete", i.e. the population form which you drew your sample. In this sense, the CI doesn't describe your sample at all, but rather the population you drew your sample from. (Somewhat more precisely, but still simplifying a bit, the CI is an interval computed from the sample using a procedure that, in at least 95% of random samples from any population, will include the population's mean. [Thanks @whuber!] But make sure to look at the comments below for some "fine print" and discussion on the definition of the CI.) You correctly got at this intuition in your question, even if you stumbled a bit on the vocabulary.
In terms of practical advice, it really depends on what you want to do. If you just want to claim that your sample was well-balanced / representative / whatever, use descriptive statistics on your sample. If you want to make inferences about certain parameters in the broader population, use inferential statistics.
Bottom line: it depends on whether you want to describe your sample or use your sample to make statements about the general population.
