In this paper, we develop and apply multi-fidelity (multi-level) statistical machine learning methods to learn differences in lattice energies of a hierarchy of simulation methods that are commonly adopted during the structure exploration and final ranking stages of CSP. A fast, approximate force field that is used at the early stages of a global structure search is set as the baseline, and the machine learning models are developed to refine its results, first to predict GGA DFT lattice energies and then hybrid DFT. In the following, we start by optimizing data gathering costs, and we then train the machine learning model on the energy differences between models in the energy hierarchy. The quality of the constructed models is verified by application to CSP for three molecular crystals known to be challenging in terms of energy ranking: oxalic acid, maleic hydrazide, and urazole.