We then trained random forest machine learning models to predict CH-p stacking interaction energies based on their orientations and found distances between the galactose carbon atoms and the aromatic ring centroids to be the most predictive features. Finally, we identified an energetic landscape for β-galactose-aromatic CH-p stacking interactions using only the distances between galactose carbon atoms and aromatic amino acid ring centroids. This landscape demonstrates that CH-p stacking interactions have high orientational flexibility with a continuous minimum energy well that corresponds to many distinct orientations. Optimal CH-p stacking interactions can be formed by maximizing favorable contacts between different subsets of hydrogen atoms and the aromatic ring(s).