# Central limit theorem with sorted samples

I recently encountered a strange situation while dealing with sampling :
Let's suppose I have X1 ... Xn random samples drawn from a population. Then I sort every samples and I make the sum of every samples. Why the sum sample seems to follow the same distribution as the original population and not a normal distribution ? Here is my python code if my question was not clear and an example with samples vs sorted samples :

n = 100
m= 100
k = 6

import numpy as np
import random
import matplotlib.pyplot as plt

array2 = np.asarray([])
for j in range (200):
array = np.asarray([0 for i in range(n)])
for i in range(m):
sample = np.asarray([np.random.lognormal() for draw in range(n)])
sample = np.sort(sample)