The workflow we have presented here is a further step towards integrating MLPs into organic CSP. By combining active learning and ∆-learning, leveraging the lower level energies describing the landscapes, which are available at no added computational cost beyond the crystal structure search, we have demonstrated a highly efficient and automatable method for generating MLPs from CSP landscapes. As shown for oxalic acid and resorcinol, active learning from a force field or DFTB baseline can achieve errors at or below 1 kJ mol -1 , using approximately 10 % of the landscape for training. We converged active learning at errors of 3-4 kJ mol -1 , as being acceptable over a much broader energy range of predicted crystal structures using only 1.2% of structures for training.