Recently, it has been common to use parallel processing for machine learning. CGRAs are drawing attention in terms of reconfigurability and high performance. We propose a method to map data-flow graphs onto CGRAs by SAT solving. The proposed method can perform the automatic transformation which changes the order of operations in data-flow graphs to obtain more efficient schedules. It also accommodates mapping of multi-node operations like MAC operation. We have solved mapping problems of matrix-vector multiplication. In our experiment, a SAT solver outperformed an ILP solver. Our method successfully processed a data-flow graph of more than a hundred nodes. The automatic transformation under the associative and commutative laws was not as much scalable but successfully reduced the number of cycles, where the XBTree-based method worked faster than the enumeration-based method. As another direction, we tried to optimize a CGRA architecture according to a data-flow graph and were able to reduce its PEs and connections through incremental SAT solving.