Insights on characteristics of an optimization problem is highly important in order to select and configure the right algorithm. Some techniques called features are defined for analyzing the fitness landscape of a problem. Despite their successes, our understanding of which features are actually relevant for the discrimination between different optimization problems is rather weak, since in most applications the features are used in a black-box manner. Another aspect that has been ignored in the exploratory landscape analysis literature is the robustness of the feature computation against the randomness of sample points from which the feature values are estimated. Moreover, the influence of the number of sample points from which the feature values are estimated is also an aspect ignored by the literature. In this paper, we study these three aspects: the robustness against the random sampling, the influence of the number of sample points, and the expressiveness in terms of ability to discriminate problems. We perform such an analysis for 7 out of the 17 features sets covered by the flacco package. Our test bed are the 24 noiseless BBOB functions. We show that some of these features seems very well-fitted for the discrimination of the problems and quite robust whereas others lack robustness and/or expressiveness, and are therefore less suitable for an automated landscape-aware algorithm selection/configuration approach.