Can a Box plot be used for finding the useful features from the dataset?
This is actually very simple: the more separation you see between the boxes, the stronger the 'predictive power' of the covariate.
Boxplots can be actually viewed as histograms looked at from the top. The box itself encompasses 50% of the data (from 25th to 75th percentile), and the line inside the box is the median. Whiskers show you the bounds of the data, up to 1.5 IQR (if I remember correctly). Anything over that range is an outlier.
Consider 2 plots: displacement and acceleration. In the displacement plot the boxes are completely separated, meaning that at least 50% of the data 'mass' is in a completely different place for mpg01='0' than for mpg01='1'. On the other hand, in acceleration the top of one box is around the median in another box - indicating poor separation.